Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling

The estimation of the vertical components of built-up areas from free Digital Elevation Model (DEM) global data filtered by multi-scale convolutional, morphological and textural transforms are generalized at the spatial resolution of 250 meters using linear least-squares regression techniques. Six test cases were selected: Hong Kong, London, New York, San Francisco, Sao Paulo, and Toronto. Five global DEM and two DEM composites are evaluated in terms of 60 combinations of linear, morphological and textural filtering and different generalization techniques. Four generalized vertical components estimates of built-up areas are introduced: the Average Gross Building Height (AGBH), the Average Net Building Height (ANBH), the Standard Deviation of Gross Building Height (SGBH), and the Standard Deviation of Net Building Height (SNBH). The study shows that the best estimation of the net GVC of built-up areas given by the ANBH and SNBH, always contains a greater error than their corresponding gross GVC estimation given by the AGBH and SGBH, both in terms of mean and standard deviation. Among the sources evaluated in this study, the best DEM source for estimating the GVC of built-up areas with univariate linear regression techniques is a composite of the 1-arcsec Shuttle Radar Topography Mission (SRTM30) and the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) using the union operator (CMP_SRTM30-AW3D30_U). A multivariate linear model was developed using 16 satellite features extracted from the CMP_SRTM30-AW3D30_U enriched by other land cover sources, to estimate the gross GVC. A RMSE of 2.40 m and 3.25 m was obtained for the AGBH and the SGBH, respectively. A similar multivariate linear model was developed to estimate the net GVC. A RMSE of 6.63 m and 4.38 m was obtained for the ANBH and the SNBH, respectively. The main limiting factors on the use of the available global DEMs for estimating the GVC of built-up areas are two. First, the horizontal resolution of these sources (circa 30 and 90 meters) corresponds to a sampling size that is larger than the expected average horizontal size of built-up structures as detected from nadir-angle Earth Observation (EO) data, producing more reliable estimates for gross vertical components than for net vertical component of built-up areas. Second, post-production processing targeting Digital Terrain Model specifications may purposely filter out the information on the vertical component of built-up areas that are contained in the global DEMs. Under the limitations of the study presented here, these results show a potential for using global DEM sources in order to derive statistically generalized parameters describing the vertical characteristics of built-up areas, at the scale of 250x250 meters. However, estimates need to be evaluated in terms of the specific requirements of target applications such as spatial population modelling, urban morphology, climate studies and so on.

Measuring and Monitoring Urban Impacts on Climate Change from Space

As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations.

The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland

This article analyzes the applicability of spatial data for evaluating and monitoring changes in land use and their impact on the local landscape. The Coordination of Information on the Environment (CORINE) Land Cover database was used to develop a procedure and an indicator for analyzing changes in land cover, and the continuity of different land use types. Changes in land use types were evaluated based on land cover data. The results were analyzed over time to track changes in the evaluated region. The studied area was the Region of Warmia and Mazury in Poland. The preservation of homogeneous land cover plays a particularly important role in areas characterized by high natural value and an abundance of forests and water bodies. The study revealed considerable changes in land cover and landscape fragmentation in the analyzed region.

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.

A Generic Classification Scheme for Urban Structure Types

This paper presents a proposal for a generic urban structure type (UST) scheme. Initially developed in the context of urban ecology, the UST approach is increasingly popular in the remote sensing community. However, there is no consistent and standardized UST framework. Until now, the terms land use and certain USTs are often used and described synonymously, or components of structure and use are intermingled. We suggest a generic nomenclature and a respective UST scheme that can be applied worldwide by stakeholders of different disciplines. Based on the insights of a rigorous literature analysis, we formulate a generic structural- and object-based typology, allowing for the generation of hierarchically and terminologically consistent USTs. The developed terminology exclusively focuses on morphology, urban structures and the general exterior appearance of buildings. It builds on the delimitation of spatial objects at several scales and leaves out all social aspects and land use aspects of an urban area. These underlying objects or urban artefacts and their structure- and object-related features, such as texture, patterns, shape, etc. are the core of the hierarchically structured UST scheme. Finally, the authors present a generic framework for the implementation of a remote sensing-based UST classification along with the requirements regarding sensors, data and data types.